code: def pad(
      img: Image.Image,
      padding: Union[int, List[int], Tuple[int, ...]],
      fill: Optional[Union[float, List[float], Tuple[float, ...]]] = 0,
      padding_mode: Literal[
          "constant", "edge", "reflect", "symmetric"
      ] = "constant",
  ) -> Image.Image:
      if not _is_pil_image(img):
          raise TypeError(f"img should be PIL Image. Got {type(img)}")
  
      if not isinstance(padding, (numbers.Number, tuple, list)):
          raise TypeError("Got inappropriate padding arg")
      if fill is not None and not isinstance(
          fill, (numbers.Number, tuple, list)
      ):
          raise TypeError("Got inappropriate fill arg")
      if not isinstance(padding_mode, str):
          raise TypeError("Got inappropriate padding_mode arg")
  
      if isinstance(padding, list):
          padding = tuple(padding)
  
      if isinstance(padding, tuple) and len(padding) not in [1, 2, 4]:
          raise ValueError(
              f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple"
          )
  
      if isinstance(padding, tuple) and len(padding) == 1:
          # Compatibility with `functional_tensor.pad`
          padding = padding[0]
  
      if padding_mode not in [
          "constant",
          "edge",
          "reflect",
          "symmetric",
      ]:
          raise ValueError(
              "Padding mode should be either constant, edge, reflect or symmetric"
          )
  
      if padding_mode == "constant":
          opts = _parse_fill(fill, img, name="fill")
          if img.mode == "P":
              palette = img.getpalette()
              image = ImageOps.expand(img, border=padding, **opts)
              image.putpalette(palette)
              return image
  
          return ImageOps.expand(img, border=padding, **opts)
      else:
          if isinstance(padding, int):
              pad_left = pad_right = pad_top = pad_bottom = padding
          if isinstance(padding, tuple) and len(padding) == 2:
              pad_left = pad_right = padding[0]
              pad_top = pad_bottom = padding[1]
          if isinstance(padding, tuple) and len(padding) == 4:
              pad_left = padding[0]
              pad_top = padding[1]
              pad_right = padding[2]
              pad_bottom = padding[3]
  
          p = [pad_left, pad_top, pad_right, pad_bottom]
          cropping = -np.minimum(p, 0)
  
          if cropping.any():
              crop_left, crop_top, crop_right, crop_bottom = cropping
              img = img.crop(
                  (
                      crop_left,
                      crop_top,
                      img.width - crop_right,
                      img.height - crop_bottom,
                  )
              )
  
          pad_left, pad_top, pad_right, pad_bottom = np.maximum(p, 0)
  
          if img.mode == "P":
              palette = img.getpalette()
              img = np.asarray(img)
              img = np.pad(
                  img,
                  ((pad_top, pad_bottom), (pad_left, pad_right)),
                  mode=padding_mode,
              )
              img = Image.fromarray(img)
              img.putpalette(palette)
              return img
  
          img = np.asarray(img)
          # RGB image
          if len(img.shape) == 3:
              img = np.pad(
                  img,
                  (
                      (pad_top, pad_bottom),
                      (pad_left, pad_right),
                      (0, 0),
                  ),
                  padding_mode,
              )
          # Grayscale image
          if len(img.shape) == 2:
              img = np.pad(
                  img,
                  ((pad_top, pad_bottom), (pad_left, pad_right)),
                  padding_mode,
              )
  
          return Image.fromarray(img)
  
imports: import numbers
  from typing import Any, Dict, List, Literal, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from PIL import Image, ImageEnhance, ImageOps
label_desc: Use image node_id 12 for calling a global function with example usage: pad(img, padding, fill, padding_mode) and returns: ImageOps, Image, image, img